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test.py
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"""
Demo script showing detections in sample images.
See README.md for installation instructions before running.
"""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import argparse
import os
import cv2
import matplotlib.pyplot as plt
import numpy as np
import tensorflow as tf
from lib.config import config as cfg
from lib.utils.nms_wrapper import nms
from lib.utils.test import im_detect,test_net
#from nets.resnet_v1 import resnetv1
from lib.nets.vgg16 import vgg16
from lib.nets.resnet_v1 import resnetv1
from lib.nets.resnet_v1_att import resnetv1
#from lib.nets.vgg16gate import vgg16
from lib.utils.timer import Timer
os.environ["CUDA_DEVICE_ORDER"] = 'PCI_BUS_ID'
os.environ["CUDA_VISIBLE_DEVICES"] = "0"
from lib.utils.test import test_net
from lib.datasets.factory import get_imdb
# CLASSES = ('__background__',
# 'aeroplane', 'bicycle', 'bird', 'boat',
# 'bottle', 'bus', 'car', 'cat', 'chair',
# 'cow', 'diningtable', 'dog', 'horse',
# 'motorbike', 'person', 'pottedplant',
# 'sheep', 'sofa', 'train', 'tvmonitor')
# CLASSES = ('__background__',
# 'finger')
CLASSES = ('__background__',
'zangpian','loujiang','duanshan','bengbian')
#'crackle')
NETS = {'vgg16': ('vgg16_faster_rcnn_iter_70000.ckpt',), 'res101': ('res101_faster_rcnn_iter_110000.ckpt',)}
DATASETS = {'pascal_voc': ('voc_2007_trainval',), 'pascal_voc_0712': ('voc_2007_trainval+voc_2012_trainval',)}
def vis_detections(det_txt, image_id, ax, im, class_name, dets, thresh=0.5):
"""Draw detected bounding boxes."""
#print("+_+")
#print(class_name,dets,thresh)
inds = np.where(dets[:, -1] >= thresh)[0]
print("!!!")
print(inds) # 是否检测出来东西，如果有的话为0如果没有为空
if len(inds) == 0:
return
#print(im.shape) # 4000 6000 3
#调整通道顺序，如果不调整通道顺序，图像就不正常
test_proposal = np.shape(dets)
num_proposal = test_proposal[0]
for i in inds:
#for i in range(num_proposal):
bbox = dets[i, :4]
score = dets[i, -1]
#print(bbox[0],bbox[1],bbox[2],bbox[3])
print("add one patch")
det_txt.write(str(image_id) + ' ' + str(score) + ' ' + str(bbox[0]) + ' ' + str(bbox[1]) + ' ' + str(
bbox[2]) + ' ' + str(bbox[3]))
det_txt.write("\n")
if class_name == 'finger':
ax.add_patch(
plt.Rectangle((bbox[0], bbox[1]),
bbox[2] - bbox[0],
bbox[3] - bbox[1], fill=False,
edgecolor='red', linewidth=2)
)
ax.text(bbox[0]-96, bbox[1] - 2,
'{:s} {:.3f}'.format(class_name, score),
bbox=dict(facecolor='blue', alpha=0.5),
fontsize=18, color='white')
else:
ax.add_patch(
plt.Rectangle((bbox[0], bbox[1]),
bbox[2] - bbox[0],
bbox[3] - bbox[1], fill=False,
edgecolor='cyan', linewidth=2)
)
ax.text(bbox[0], bbox[1] - 2,
'{:s} {:.3f}'.format(class_name, score),
bbox=dict(facecolor='blue', alpha=0.5),
fontsize=18, color='white')
ax.set_title(('{} detections with '
'p({} | box) >= {:.1f}').format(class_name, class_name,thresh),fontsize=12)
def demo(sess, net, image_name, det_txt):
"""Detect object classes in an image using pre-computed object proposals."""
# Load the demo image
im_file = os.path.join(cfg.FLAGS2["data_dir"], 'demo_bengbian', image_name)
im = cv2.imread(im_file)
# Detect all object classes and regress object bounds
timer = Timer()
timer.tic()
# detect the picture to find score and boxes
scores, boxes = im_detect(sess, net,im,image_name)
# 检测主体部分,在这里加上save_feature_picture
# 这里的net内容是vgg
timer.toc()
print('Detection took {:.3f}s for {:d} object proposals'.format(timer.total_time, boxes.shape[0]))
# Visualize detections for each class
CONF_THRESH = 0.5
NMS_THRESH = 0.1
im = im[:, :, (2, 1, 0)]
fig, ax = plt.subplots(figsize=(10,10))
ax.imshow(im, aspect='equal')
image_id = image_name.split('.')[0]
for cls_ind, cls in enumerate(CLASSES[1:]):
cls_ind += 1 # because we skipped background
cls_boxes = boxes[:, 4 * cls_ind:4 * (cls_ind + 1)]
cls_scores = scores[:, cls_ind]
dets = np.hstack((cls_boxes,
cls_scores[:, np.newaxis])).astype(np.float32)
keep = nms(dets, NMS_THRESH)
dets = dets[keep, :]
inds = np.where(dets[:, -1] >= 0.5)[0]
print("!!!")
print(inds) # 是否检测出来东西，如果有的话为0如果没有为空
if len(inds) == 0:
a = 1
else:
a = 0
vis_detections(det_txt, image_id, ax,im, cls, dets, thresh=CONF_THRESH)
# vis_detections(det_txt, image_id, ax, im, cls, dets, thresh=CONF_THRESH)
plt.draw()
return a
def parse_args():
"""Parse input arguments."""
parser = argparse.ArgumentParser(description='Tensorflow Faster R-CNN demo')
parser.add_argument('--net', dest='demo_net', help='Network to use [vgg16 res101]',
choices=NETS.keys(), default='resnetv1')
parser.add_argument('--dataset', dest='dataset', help='Trained dataset [pascal_voc pascal_voc_0712]',
choices=DATASETS.keys(), default='pascal_voc')
args = parser.parse_args()
return args
if __name__ == '__main__':
args = parse_args()
# model path
demonet = args.demo_net
dataset = args.dataset
# tfmodel = os.path.join('output', demonet, DATASETS[dataset][0], 'default', NETS[demonet][0])
# tfmodel=r'D:\Users\Binyi_Su\Experiments\Faster-rcnn\Faster-RCNN-TensorFlow-Python3.5-master\output\vgg16\voc_2007_trainval\default\vgg16_faster_rcnn_iter_20000.ckpt'
#tfmodel='default18/voc_2007_trainval/default/vgg16_faster_rcnn_iter_40000.ckpt'
tfmodel = 'default_resnet/voc_2007_trainval/resnetv1-att/vgg16_faster_rcnn_iter_40000.ckpt'
#tfmodel ='D://Users//lc//Faster - RCNN - lc//default_resnet_att//voc_2007_trainval//resnetv1//vgg16_faster_rcnn_iter_40000.ckpt'
if not os.path.isfile(tfmodel + '.meta'):
print(tfmodel)
raise IOError(('{:s} not found.\nDid you download the proper networks from '
'our server and place them properly?').format(tfmodel + '.meta4'))
# set config
tfconfig = tf.ConfigProto(allow_soft_placement=True)
tfconfig.gpu_options.allow_growth = True
# init session
sess = tf.Session(config=tfconfig)
# load network
if demonet == 'vgg16':
net = vgg16(batch_size=1)
elif demonet == 'resnetv1':
net = resnetv1(batch_size=1, num_layers=50)
else:
raise NotImplementedError
layers = net.create_architecture(sess, "TEST", 5,
tag='default', anchor_scales=[8, 16, 32])
# spatial_attention = tf.reshape(spatial_attention,[32, 32])
print(layers)
# plt.imshow(spatial_attention)
saver = tf.train.Saver()
saver.restore(sess, tfmodel)
print('Loaded network {:s}'.format(tfmodel))
# im_names = ['65.bmp', '66.bmp', '67.bmp', '68.bmp',
# '69.bmp', '70.bmp','71.bmp','72.bmp','73.bmp','74.bmp','75.bmp']
# im_names = ['1.jpg', '2.jpg', '3.jpg', '4.jpg',
# '5.jpg','6.jpg', '7.jpg', '8.jpg', '9.jpg', '10.jpg']
im_names = os.listdir('F:\\fastercnn\\data\\demo_bengbian')
det_txt = open(
"F:\\fastercnn\\default_resnet\\test-txt\\resnet50_att_bengbian.txt", 'w')
sum = 0
total = 0
for im_name in im_names:
print('~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~')
print('Demo for data/demo/{}'.format(im_name))
a = demo(sess, net, im_name, det_txt)
total = total + 1
sum = sum+a
# demo(sess, net, im_name, det_txt)
# output RPCAN feature map
# spatial_attention = np.reshape(spatial_attention, (38, 38))
# spatial_attention = cv2.resize(spatial_attention,(128,128))
# plt.imshow(spatial_attention)
# plt.imsave('C:\\Users\\Administrator\\Desktop\\feature_map\\a.jpg', spatial_attention)
plt.axis('off')
plt.savefig("default_resnet/testatt/finish"+im_name)
# imdb = get_imdb("voc_2007_trainval")
# test_net(sess, net, imdb, 'default')
# plt.show()
print("sum=",sum)
print("total=", total)